1. Field of the Invention
The present invention relates to an apparatus for correcting a phase of a phased array antenna and a method thereof; and, more particularly, to an apparatus for correcting a phase of a phased array antenna and a method thereof, which detect overall power intensity of a received signal, and estimates and corrects a phase error of each radiation element to maximize the detected power intensity.
The present invention also relates to a method for correcting a phase error of a phased array antenna and detecting a arrival direction of a radio signal.
The present invention also relates to a genetic algorithm for detecting a phase error to maximize a voltage value.
This work was supported by the IT R&D program of MIC/IITA [2007-F-041-01, “Intelligent Antenna Technology Development”].
2. Description of Related Art
Hereinafter, a basic theory of a genetic algorithm will be described.
In nature, populations have been evolving for many years. Each of the populations is a set of individuals of a predetermined generation, and predetermined individuals having high fitness for a given environment have the large chance to survive and to reproduce among the populations. Here, populations of the next generation may be created through crossover and mutation.
In a genetic algorithm (GA), the number of individuals is referred as a population size. Each individual has chromosome formed of a plurality of genes. A locus is a position that a given gene occupies on a chromosome. An allele is one member of a pair or series of genes that occupy a predetermined position on a predetermined chromosome. Characteristics of a predetermined population are decided by chromosomes. For example, a person has black hair because the person has a predetermined chromosome combination of black hair characteristic.
An allele that is decided by genes is referred as phenotype, and a corresponding chromosome structure is referred as a genotype. A complicated form of the phenotype is decided by a plurality of locuses. The complicated form of the phenotype is referred as epistasis. Converting phenotype to genotype is referred as coding, and converting genotype to phenotype is referred as decoding.
Such biological evolution has been imitated and artificially modeled to an algorithm. Such an algorithm is referred as a genetic algorithm.
The genetic algorithm is one type of a solution search method and an optimization method. That is, a solution set is formed by encoding a solution for a given problem to an individual, and a population is formed with the individuals. Then, a new solution is generated through crossover and mutation of individuals, and a fitness of the new solution is analyzed, thereby generating an optimal solution. Ending conditions of the genetic algorithm may be if evolution has been progressed for the predetermined number of generations, if fitness has not been improved for the plurality of generations, or if fitness becomes higher than a predetermined threshold.
The genetic algorithm has been used to find an optimal solution for a Non-Linear Problem (NLP), a Nondeterministic Polynomial time—Complete problem (NP-complete), and Nondeterministic polynomial time—hard problem (NP-Hard), which have been known as non-solvable problems or problems with high computational complexity.
The generic algorithm includes exploration for exploring an unknown area and exploitation for obtaining valid information. Therefore, the harmony of exploration and exploitation is very important for obtaining an optimal solution of a problem. Using the obtained information is very similar to hill-climbing. Also, the generic algorithm has the same characteristics of random search as exploration is emphasized more.
The genetic algorithm is an algorithm that can control the above two conditions, the exploration and the exploitation, together. A population size M, a probability of crossover pc and a probability of mutation pm are major parameters for controlling the two conditions.
Since high probabilities of crossover and mutation ps and ms improve exploration ability, it is advantageous to find a search area having high fitness at an initial stage. However, the high probabilities of crossover and mutation ps and ms deteriorate exploitation ability, thereby decreasing a convergence speed of converging a good solution to an optimal solution in a search space after finding a predetermined level of the good solution. Here, the low probabilities of crossover and mutation pc and pm have the opposite characteristics.
If a population size M is small, it is possible to reduce a time for calculating fitness. However, a solution may be converged before calculating the optimal solution due to fast loss of diversity of individuals. On the contrary, if the population size M is large, a probability of reaching an optimal solution is high too. However, a large memory space and a long calculating time are required. A method for deciding an optimal population size that satisfies the performance evaluation factors may differ according to the characteristic of a problem and other control parameters.
A phased array antenna includes a plurality of active elements. That is, a plurality of array radiation elements, a shifter, an attenuator, and a low noise amplifier/high power amplifier, and a combiner/divider are connected through a coaxial cable in the phased array antenna.
All of phased array antennas have a relative phase error due to path difference of each channel. Also, a position error of array radiation element is generated due to manufacture processes or deformation. These errors act as comparative phase errors for each channel of array element, thereby causing antenna gain reduction, side lobe increment, and primary beam polarization.
Therefore, there have been demands for developing a method for automatically correcting a phase error of each channel at high speed in a phased array antenna.
In order to correct the phase error of the phased array antenna, a method for finding a phase correction value from all bit combinations to optimize a radiation pattern for a phased array antenna having a digital phase shifter was introduced.
However, this method needs a long time to find a phase correction value although the number of array elements is only about 10. Also, it is impossible to use this method for an analog phase shifter.
In order to overcome such shortcomings, another method was introduced. In this method, one radiation element is turned on and the others are turned off. Then, a phase of each channel having the turned-on radiation element is measured using a network analyzer. A correction value is calculated based on the measured value.
However, it is difficult to use the network analyzer if a phased array antenna is big because a distance for satisfying a far-field condition may be longer than several tens meters.
Furthermore, a method for correcting a phase error caused by temperature in an array antenna was introduced in an article by ‘Y. Kuwahara’, entitled ‘Phased Array Antenna with Temperature Compensating Capability’, IEEE International Symposium on Phased Array Systems and Technology, pp. 21-26, October 1996.
Moreover, another method for correcting a phase is introduced in an article by ‘H. M. Aumann’ et. al., entitled ‘Phased Array Antenna Calibration and Pattern Prediction Using Mutual Coupling Measurement’ IEEE Transactions on Antennas and Propagation, vol. 37, no. 7, pp. 844-850, July 1999.
However, these methods require long time and great labor to correct the phase error. Therefore, there is limitation to use such methods for a phase array antenna having a plurality of radiation elements. That is, these methods according to the related art have low efficiency when these methods are applied for a phased array antenna having a plurality of radiation elements.